Tuberculosis (TB) is caused by Mycobacterium tuberculosis (M.tb) complex (MTBC), which includes M.tb as one of the causative bacteria. In contrast, non-tuberculous mycobacteria (NTM) refers to Mycobacterium spp. that do not cause TB or leprosy (MTBC spp., M . leprae , and M . lepromatosis ). Mycobacterium spp. are responsible for the deadliest infections and remain a significant challenge in diagnosis and treatment. Mycobacterium spp. have developed multiple complementary mechanisms to defend against antibiotics. Specifically, the mechanisms include modifying the drug target sites, enzymatically inactivating the drugs, and lowering intracellular antibiotic concentrations by overexpressing efflux pumps. These adaptations contribute to the emergence of multi-drug resistant pathogens. This review provides an overview of antibiotic resistance in Mycobacterium spp. with a focus on several key factors, such as enzyme-mediated antibiotic deactivation, gene expression, biofilm formation, and the role of efflux pumps. A critical objective of this review includes Mycobacterium efflux pumps, the significant role in antibiotic resistance, and compounds that act against these efflux pumps.

Cloud-based financial exchanges require sub-10.... device-to-device clock synchronization accuracy while adhering to Coordinated Universal Time (UTC). Existing clock sync techniques struggle to meet this demand at scale and are vulnerable to clock drift, jitter, and path asymmetries. Firefly, a software-driven datacenter clock sync system, scalably, cost-effectively, and reliably achieves very high clock sync accuracy. It employs a distributed consensus algorithm on a random overlay graph to rapidly converge to a common time while applying gradual adjustments to device hardware clocks. To realize consistent sync-to-UTC (external sync) across devices while maintaining a stable device-to-device internal sync, Firefly uses a novel technique, layered synchronization, that decouples internal and external syncs. In a 248-machine Clos network, Firefly achieves sub-10ns device-to-device and <= 1 mu s device-to-UTC sync, and is resilient to time server failure and unstable clocks.

Influenza outbreaks cause pandemics in millions of people. The treatment of influenza remains a challenge due to significant genetic polymorphism in the influenza virus. Also, developing vaccines to protect against seasonal and pandemic influenza infections is constantly impeded. Thus, antibiotics are the only first line of defense against antigenically distinct strains or new subtypes of influenza viruses. Among several anti-influenza targets, the M2 protein of the influenza virus performs several activities. M2 protein is an ion channel that permits proton conductance through the virion envelope and the deacidification of the Golgi apparatus. Both these functions are critical for viral replication. Thus, targeting the M2 protein of the influenza virus is an essential target. Rimantadine and amantadine are two well-known drugs that act on the M2 protein. However, these drugs acquired resistance to influenza and thus are not recommended to treat influenza infections. This review discusses an overview of anti-influenza therapy, M2 ion channel functions, and its working principle. It also discusses the M2 structure and its role, and the change in the structure leads to mutant variants of influenza A virus. We also shed light on the recently identified compounds acting against wild-type and mutated M2 proteins of influenza virus A. These scaffolds could be an alternative to M2 inhibitors and be developed as antibiotics for treating influenza infections.

A series of 1,1'-biphenyl-3-carboxamide and furan-phenyl-carboxamide analogs were synthesized using an optimized scheme and confirmed by 1H and 13C nuclear magnetic resonance and high-resolution mass spectrometry techniques. The synthesized peptidomimetics analogs were screened in vitro to understand the inhibitory potential of pancreatic lipase (PL). Analogs were assessed for the PL inhibitory activity based on interactions, geometric complementarity, and docking score. Among the synthesized analogs, 9, 29, and 24 were found to have the most potent PL inhibitory activity with IC50 values of 3.87, 4.95, and 5.34 µM, respectively, compared to that of the standard drug, that is, orlistat, which inhibits PL with an IC50 value of 0.99 µM. The most potent analog, 9, exhibited a competitive-type inhibition with an inhibition constant (Ki) of 2.72 µM. In silico molecular docking of analog 9 with the PL (PDB ID:1LPB) showed a docking score of -11.00 kcal/mol. Analog 9 formed crucial hydrogen bond interaction with Ser152, His263, π-cation interaction with Asp79, Arg256, and π-π stacking with Phe77, Tyr114 at the protein's active site. The molecular dynamic simulation confirmed that analog 9 forms stable interactions with PL at the end of 200 ns with root mean square deviation values of 2.5 and 6 Å. No toxicity was observed for analog 9 (concentration range of 1-20 µM) when tested by MTT assay in RAW 264.7 cells.

Rheumatoid arthritis (RA) is a complex and challenging autoimmune disease characterized by chronic inflammation of the joints, discomfort, stiffness, functional impairment, and systemic complications that affect millions of people around the world. Despite advances in medication, controlling RA remains difficult due to its complicated pathophysiology and numerous clinical symptoms. Synthetic medications, while effective, frequently cause considerable adverse effects, necessitating the investigation of alternate therapeutic options. This study attempts to provide a complete overview of synthetic medications and natural items used to treat RA. It specifically investigates the pathophysiological mechanisms that underpin RA and the efficacy and safety profiles of synthetic pharmaceuticals. Synthetic medications, such as disease-modifying antirheumatic drugs (DMARDs), and biologics remain important treatments for RA, albeit with hazards. So to explore safer and more effective therapy for the treatment of RA, a need to exploit the possible therapeutic benefits of natural items such as antioxidants, plant secondary metabolites, and traditional herbal remedies arises. By encompassing a spectrum of insights, from the molecular level to holistic traditional practices, this review aims to provide a holistic understanding of the role of natural products and traditional herbal medicines in the managerial landscape of RA. Furthermore, it investigates the effect of nutrition in regulating inflammation and disease development in RA. Integrative techniques that use natural products present intriguing adjuvant therapy, delivering anti-inflammatory, analgesic, and immunomodulatory effects with potentially fewer side effects. Understanding the interaction of synthetic medications and natural products, as well as the role of nutrition, can help enhance RA treatment regimens, improve patient outcomes, and reduce treatment-related problems. This study is a valuable resource for doctors, researchers, and patients looking for evidence-based methods for RA care. The synthesis of this knowledge contributes to the ongoing pursuit of enhanced therapeutic strategies, fostering improved outcomes and quality of life for individuals grappling with rheumatoid arthritis.

The epidermal growth factor receptor (EGFR) is a transmembrane receptor tyrosine kinase (RTK) that maintains normal tissues and cell signaling pathways. EGFR is overactivated and overexpressed in many malignancies, including breast, lung, pancreatic, and kidney. Further, the EGFR gene mutations and protein overexpression activate downstream signaling pathways in cancerous cells, stimulating the growth, survival, resistance to apoptosis, and progression of tumors. Anti-EGFR therapy is the potential approach for treating malignancies and has demonstrated clinical success in treating specific cancers. The recent report suggests most of the clinically used EGFR tyrosine kinase inhibitors developed resistance to the cancer cells. This perspective provides a brief overview of EGFR and its implications in cancer. We have summarized natural products-derived anticancer compounds with the mechanistic basis of tumor inhibition via the EGFR pathway. We propose that developing natural lead molecules into new anticancer agents has a bright future after clinical investigation.

Nontuberculous Mycobacteria (NTM) refer to bacteria other than all Mycobacterium species that do not cause tuberculosis or leprosy, excluding the species of the Mycobacterium tuberculosis complex, M. leprae and M. lepromatosis. NTM are ubiquitous and present in soils and natural waters. NTM can survive in a wide range of environmental conditions. The direct inoculum of the NTM from water or other materials is most likely a source of infections. NTMs are responsible for several illnesses, including pulmonary alveolar proteinosis, cystic fibrosis, bronchiectasis, chronic obstructive pneumoconiosis, and pulmonary disease. Recent reports suggest that NTM species have become insensitive to sterilizing agents, antiseptics, and disinfectants. The efficacy of existing anti-NTM regimens is diminishing and has been compromised due to drug resistance. New and recurring cases of multidrug-resistant NTM strains are increasing. Thus, there is an urgent need for ant-NTM regimens with novel modes of action. This review sheds light on the mode of antimicrobial resistance in the NTM species. Then, we discussed the repurposable drugs (antibiotics) that have shown new indications (activity against NTM strains) that could be developed for treating NTM infections. Also, we have summarised recently identified natural leads acting against NTM, which have the potential for treating NTM-associated infections.

This literature commences with an overview of electric vehicles, encompassing their diverse types, charging topologies, and future prospects. Subsequently, the paper delves into the dynamics of vehicle motion, elucidating the forces at play during acceleration, with simulations conducted using MATLAB/Simulink. Visual representations of electric vehicle systems are provided, serving as a precursor to forthcoming Simulink realizations. Furthermore, the paper outlines the dynamic mathematical modelling of electric vehicles in subsequent sections. The discussion transitions to the simulation and mathematical analysis of various converters utilized in electric vehicles, including rectifiers, inverters, and choppers. Battery-related calculations are then explored, addressing the requisite number of cells in series and parallel configurations for optimal vehicle operation. Finally, the project scrutinizes three potential issues—cell equalization, voltage limits violation, and thermal anomalies in the battery— during vehicle operation or charging. A simulation model is presented as a proposed solution to mitigate these challenges, underscoring the comprehensive approach of this research endeavor.

Acinetobacter baumannii is one of the deadliest Gram-negative bacteria (GNB), responsible for 2-10% of hospital-acquired infections. Several antibiotics are used to control the growth of A. baumannii. However, in recent decades, the abuse and misuse of antibiotics to treat non-microbial diseases have led to the emergence of multidrug-resistant A. baumannii strains. A. baumannii possesses a complex cell wall structure. Cell wall-targeting agents remain the center of antibiotic drug discovery. Notably, the antibacterial drug discovery intends to target the membrane of the bacteria, offering several advantages over antibiotics targeting intracellular systems, as membrane-targeting agents do not have to travel through the plasma membrane to reach the cytoplasmic targets. Microorganisms, insects, and mammals produce antimicrobial peptides as their first line of defense to protect themselves from pathogens and predators. Importantly, antimicrobial peptides are considered potential alternatives to antibiotics. This communication summarises the recently identified peptides of natural origin and their synthetic congeners acting against the A. baumannii membrane by cell wall disruption.

The difficulty in gaining visibility into the fine-timescale hop-level congestion state of networks has been a key challenge faced by congestion control (CC) protocols for decades. However, the emergence of commodity switches supporting in-network telemetry (INT) enables more advanced CC. In this paper, we present Poseidon, a novel CC protocol that exploits INT to address blind spots of CC algorithms and realize several fundamentally advantageous properties. First, Poseidon is efficient: it achieves low queuing delay, high throughput, and fast convergence. Furthermore, Poseidon decouples bandwidth fairness from the traditional AIMD control law, using a novel adaptive update scheme that converges quickly and smooths out oscillations. Second, Poseidon is robust: it realizes CC for the actual bottleneck hop, and achieves maxmin fairness across traffic patterns, including multi-hop and reverse-path congestion. Third, Poseidon is practical: it is amenable to incremental brownfield deployment in networks that mix INT and non-INT switches. We show, via testbed and simulation experiments, that Poseidon provides significant improvements over the state-of-the-art Swift CC algorithm across key metrics - RTT, throughput, fairness, and convergence - resulting in end-to-end application performance gains. Evaluated across several scenarios, Poseidon lowers fabric RTT by up to 50%, reduces time to converge up to 12x, and decreases throughput variation across flows by up to 70%. Collectively, these improvements reduce message transfer time by more than 61% on average and 14.5x at 99.9p.

Tuberculosis (TB) remains one of the deadliest infectious diseases caused by Mycobacterium tuberculosis ( M.tb ). It is responsible for significant causes of mortality and morbidity worldwide. M.tb possesses robust defense mechanisms against most antibiotic drugs and host responses due to their complex cell membranes with unique lipid molecules. Thus, the efficacy of existing front‐line drugs is diminishing, and new and recurring cases of TB arising from multidrug‐resistant M.tb are increasing. TB begs the scientific community to explore novel therapeutic avenues. A precise knowledge of the compounds with their mode of action could aid in developing new anti‐TB agents that can kill latent and actively multiplying M.tb . This can help in the shortening of the anti‐TB regimen and can improve the outcome of treatment strategies. Natural products have contributed several antibiotics for TB treatment. The sources of anti‐TB drugs/inhibitors discussed in this work are target‐based identification/cell‐based and phenotypic screening from natural products. Some of the recently identified natural products derived leads have reached clinical stages of TB drug development, which include rifapentine, CPZEN‐45, spectinamide‐1599 and 1810. We believe these anti‐TB agents could emerge as superior therapeutic compounds to treat TB over known Food and Drug Administration drugs.

Covering: 2015 to 2022 Staphylococcus aureus (S. aureus) is responsible for several community and hospital-acquired infections with life-threatening complications such as bacteraemia, endocarditis, meningitis, liver abscess, and spinal cord epidural abscess. In recent decades, the abuse and misuse of antibiotics in humans, animals, plants, and fungi and the treatment of nonmicrobial diseases have led to the rapid emergence of multidrug-resistant pathogens. The bacterial wall is a complex structure consisting of the cell membrane, peptidoglycan cell wall, and various associated polymers. The enzymes involved in bacterial cell wall synthesis are established antibiotic targets and continue to be a central focus for antibiotic development. Natural products play a vital role in drug discovery and development. Importantly, natural products provide a starting point for active/lead compounds that sometimes need modification based on structural and biological properties to meet the drug criteria. Notably, microorganisms and plant metabolites have contributed as antibiotics for noninfectious diseases. In this study, we have summarized the recent advances in understanding the activity of the drugs or agents of natural origin that directly inhibit the bacterial membrane, membrane components, and membrane biosynthetic enzymes by targeting membrane-embedded proteins. We also discussed the unique aspects of the active mechanisms of established antibiotics or new agents.

Data center networks are inclined towards increasing line rates to 200Gbps and beyond to satisfy the performance requirements of applications such as NVMe and distributed ML. With larger Bandwidth Delay Products (BDPs), an increasing number of transfers fit within a few BDPs. These transfers are not only more performance-sensitive to congestion, but also bring more challenges to congestion control (CC) as they leave little time for CC to make the right decisions. Therefore, CC is under more pressure than ever before to achieve minimal queuing and high link utilization, leaving no room for imperfect control decisions. We identify that for CC to make quick and accurate decisions, the use of precise congestion signals and minimization of the control loop delay are vital. We address these issues by designing Bolt, an attempt to push congestion control to its theoretical limits by harnessing the power of programmable data planes. Bolt is founded on three core ideas, (i) Sub-RTT Control (SRC) reacts to congestion faster than RTT control loop delay, (ii) Proactive Ramp-up (PRU) foresees flow completions in the future to promptly occupy released bandwidth, and (iii) Supply matching (SM) explicitly matches bandwidth demand with supply to maximize utilization. Our experiments in testbed and simulations demonstrate that Bolt reduces 99(th)-p latency by 80% and improves 99(th)-p flow completion time by up to 3x compared to Swift and HPCC while maintaining near line-rate utilization even at 400Gbps.

Staphylococcus aureus (S. aureus) is a pathogen responsible for various community and hospital-acquired infections with life-threatening complications like bacteraemia, endocarditis, meningitis, liver abscess, and spinal cord epidural abscess. Antibiotics have been used to treat microbial infections since the introduction of penicillin in 1940. In recent decades, the abuse and misuse of antibiotics in humans, animals, plants, and fungi, including the treatment of non-microbial diseases, have led to the rapid emergence of multidrug-resistant pathogens with increased virulence. Bacteria have developed several complementary mechanisms to avoid the effects of antibiotics. These mechanisms include chemical transformations and enzymatic inactivation of antibiotics, modification of antibiotics' target site, and reduction of intracellular antibiotics concentration by changes in membrane permeability or by the overexpression of efflux pumps (EPs). The strategy to check antibiotic resistance includes synthesis of the antibiotic analogues, or antibiotics are given in combination with the adjuvant. The inhibitors of multidrug EPs are considered promising alternative therapeutic options with the potential to revive the effects of antibiotics and reduce bacterial virulence. Natural products played a vital role in drug discovery and significantly contributed to the area of infectious diseases. Also, natural products provide lead compounds that sometimes need modification based on structural and biological properties to meet the drug criteria. This review discusses natural products and their derived compounds as NorA efflux pump inhibitors (EPIs).

With the increasing popularity of disaggregated storage and microservice architectures, high fan-out and fan-in Remote Procedure Calls (RPCs) now generate most of the traffic in modern datacenters. While the network plays a crucial role in RPC performance, traditional traffic classification categories cannot sufficiently capture their importance due to wide variations in RPC characteristics. As a result, meeting service-level objectives (SLOs), especially for performance-critical (PC) RPCs, remains challenging.

In the current study, the latest class of titanium alloy beta-phase (Ti-Nb-Ta-Zr) using orthopaedic device vac-uum arc melting methods. The alloy is repeatedly melted to strengthen its homogeneity. To ensure homogeneity, the ss-phase Ti-Nb-Ta-Zr alloy was replenished 4 times using the electric arch. The analysis of the beta-phase as-developed titanium alloy's microstructure, elemental and phase composition was examined. Vickers Micro-hardness tester and a universal testing machine have calculated the micro-hardness and tensile strength of the formed beta-phase titanium alloy (UTM, BIS). The metastatic beta-Phase alloy with a coarse kernel range of similar to 250 mu m was found in the micro-structure study. The crystallographic analysis revealed that the metastable '6' stage in the matrix was beta-TNTZ alloy. The mechanical properties include the 590 MPa train strength (UTS) with an elongation of 13.47 percent, and are smaller than those in Ti-6Al-4 V alloy and SS-316L with a 55 MPa young modulus near the cortical bone (approximately 10-30GPa). The surface properties were improved after heat treatment. The mechanical properties of beta-Ti alloy relative to other titanium alloys for biomedical application. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Con-ference on Functional Material, Manufacturing and Performances.

The objective of this work is to find out the optimised parameters to modify the surface properties of EN353 steel by increasing the hardness, and minimizing the loss in wear volume and enhancing the friction resistance in the material. The heat-treated samples of bare substrates were further plated with hard chromium followed by plasma nitriding to modify the surface of the EN353 steel. The nitrided chrome plated specimens were tested in 3.5 wt. % NaCl to test the corrosion properties. Microhardness and tribological properties results indicated that the plasma nitrided chromium plated EN353 steel offers higher hardness and low coefficient of friction when compared to other specimens used in the current study. The microstructure, wear and corrosion studies revealed that the modified layers reduce cracking and spalling on EN353 gear surfaces while electrochemical studies demonstrated an excellent corrosion resistance for the plasma nitrided EN353 steel. The usability of hard chromium plated and plasma nitrided EN353 material in place of hardened one improves the service life of the automotive gear application.

When used in conjunction with the current floorplan and the optimization technique in circuit design engineering, this research allows for the evaluation of design parameters that can be used to reduce congestion during integrated circuit fabrication. Testing the multiple alternative consequences of IC design will be extremely beneficial in this situation, as will be demonstrated further below. If the importance of placement and routing congestion concerns is underappreciated, the IC implementation may experience significant nonlinear problems throughout the process as a result of the underappreciation of placement and routing congestion concerns. The use of standard optimization techniques in integrated circuit design is not the most effective strategy when it comes to precisely estimating nonlinear aspects in the design of integrated circuits. To this end, advanced tools such as Xilinx VIVADO and the ICC2 have been developed, in addition to the ICC1 and VIRTUOSO, to explore for computations and recover the actual parameters that are required to design optimal placement and routing for well-organized and ordered physical design. Furthermore, this work employs the perimeter degree technique (PDT) to measure routing congestion in both horizontal and vertical directions for a silicon chip region and then applies the technique to lower the density of superfluous routing (DSR) (PDT). Recently, a metaheuristic approach to computation has increased in favor, particularly in the last two decades. It is a classic graph theory problem, and it is also a common topic in the field of optimization. However, it does not provide correct information about where and how nodes should be put, despite its popularity. Consequently, in conjunction with the optimized floorplan data, the optimized model created by the Improved Harmonic Search Optimization algorithm undergoes testing and investigation in order to estimate the amount of congestion that occurs during the routing process in VLSI circuit design and to minimize the amount of congestion that occurs.

We present a new, host-based design for link load balancing and report the first experiences of link imbalance in datacenters. Our design, PLB (Protective Load Balancing), builds on transport protocols and ECMP/WCMP to reduce network hotspots. PLB randomly changes the paths of connections that experience congestion, preferring to repath after idle periods to minimize packet reordering. It repaths a connection by changing the IPv6 Flow Label on its packets, which switches include as part of ECMP/WCMP. Across hosts, this action drives down hotspots in the network, and lowers the latency of RPCs. PLB is used fleetwide at Google for TCP and Pony Express traffic. We could deploy it when other designs were infeasible because PLB requires only small transport modifications and switch configuration changes, and is backwards-compatible. It has produced excellent gains: the median utilization imbalance of highly-loaded ToR uplinks in Google datacenters fell by 60%, packet drops correspondingly fell by 33%, and the tail latency (99p) of small RPCs fell by 20%. PLB is also a general solution that works for settings from datacenters to backbone networks, as well as different transports.

There has been significant progress in the field of sentiment analysis. However, aspect-based sentiment analysis (ABSA) has not been explored in the Japanese language even though it has a huge scope in many natural language processing applications such as 1) tracking sentiment towards products, movies, politicians etc; 2) improving customer relation models. The main reason behind this is that there is no standard Japanese dataset available for ABSA task. In this paper, we present the first standard Japanese dataset for the hotel reviews domain. The proposed dataset contains 53,192 review sentences with seven aspect categories and two polarity labels. We perform experiments on this dataset using popular ABSA approaches and report error analysis. Our experiments show that contextual models such as BERT works very well for the ABSA task in the Japanese language and also show the need to focus on other NLP tasks for better performance through our error analysis.